Noise suppression

Abstract

Noise suppression (speech enhancement) by spectral amplitude filtering using a gain determined with a quantized estimated signal-to-noise ratio plus, optionally, prior frame suppression. The relation between signal-to-noise ratio and filter gain derives from a codebook mapping with a training set constructed from clean speech and noise conditions.

1 . A method of noise suppression, comprising: 
 (a) transforming a block of input speech to a frequency domain;    (b) for each frequency, estimating the signal-to-noise ratio of said transformed speech;    (c) for said each frequency, multiplying said transformed speech by a gain factor, where said gain factor is from a lookup table indexed by a quantization of said estimated signal-to-noise ratio from (b);    (d) inverse transforming the products of the multiplyings from (c);    (e) repeating (a)-(d) for successive blocks of input speech; and    (f) combining the results of (e).    
   
   
       2 . The method of  claim 1 , wherein: 
 (a) said estimating a signal-to-noise ratio of (b) of  claim 1  uses a noise spectrum estimate updated by upward and downward time constants.    
   
   
       3 . The method of  claim 1 , wherein: 
 (a) said blocks of input speech overlap and include windowing.    
   
   
       4 . The method of  claim 1 , wherein: 
 (a) sid lookup table is also indexed by a quantization of the gain and estimated signal-to-noise ratio of a prior block of input speech.    
   
   
       5 . The method of  claim 1 , wherein: 
 (a) said gain is clamped by a minimum gain.    
   
   
       6 . The method of  claim 1 , further comprising: 
 (a) detecting voice activity in said block of input speech; and    (b) when said detection indicates no speech, increment a noise spectrum estimate for said estimating a signal-to-noise ratio of (b) of  claim 1 .    
   
   
       7 . A noise suppressor, comprising: 
 (a) a transformer for an input block of noisy speech;    (b) a noise spectrum estimator coupled to said transformer;    (c) a signal-to-noise estimator coupled to said noise spectrum estimator and to said transformer;    (d) a gain lookup table with input coupled to said signal-to-noise estimator, said gain lookup table contents being a codebook mapping from signal-to-noise ratio codebook to gain codebook and constructed from a training set of speech and noise conditions;    (e) a multiplier coupled to said transformer and to an output of said gain lookup table; and    (f) an inverse transformer coupled to an output of said multiplier.    
   
   
       8 . The noise suppressor of  claim 7 , further comprising: 
 (a) a memory for prior block estimated signal-to-noise ratio and prior block ideal gain, said memory coupled to said signal-to-noise estimator and to said lookup table; and    (b) wherein said gain lookup table includes a second input for said memory contents.    
   
   
       9 . The noise suppressor of  claim 7 , wherein: 
 (a) said noise spectrum estimator and said signal-to-noise estimator are implemented as programs on a programmable processor.    
   
   
       10 . A method of noise suppression codebook mapping, comprising: 
 (a) providing a training set of speech and noise conditions mixed to give noisy speech and corresponding ideal (noise-suppressed) speech;    (b) transforming both a block of noisy speech and a corresponding block of ideal speech to a frequency domain;    (c) for each frequency, estimating the signal-to-noise ratio of said transformed noisy speech;    (d) for said each frequency, computing an ideal gain from said transformed noise speech and said transformed ideal speech;    (e) repeating (b)-(d) for successive blocks;    (f) clustering the results of (e) to define a codebook mapping from estimated signal-to-noise to ideal gain.    
   
   
       11 . The method of  claim 10 , wherein: 
 (a) said clustering is by 
 (i) quantizing said estimated signal-to-noise results from said repeated (c) of  claim 10  to define a codebook for estimated signal-to-noise ratio; and  
 (ii) for each quantization from (i), averaging said results from repeated (d) of  claim 10  which correspond to said estimated signal-to-noise results of said repeated (c) of  claim 1  for said each quantization to define a gain codebook and a mapping from said codebook for estimated signal-to-noise ratio.  
   
   
   
       12 . The method of  claim 10 , further comprising: 
 (a) after said (d) and before said (e) of  claim 10 , for said each frequency computing the product of said estimated signal-to-noise ratio multiplied by said ideal gain from a prior block;    (b) modifying said (e) of  claim 10  to include foregoing (a); and    (c) wherein said (f) of  claim 10  codebook mapping also maps from prior block product of estimated signal-to-noise ratio multiplied by ideal gain.